Platforms optimize for their own goals, not yours. Don't mistake their vanity metrics (views, likes) for key business drivers. A clip with 100 million views can have an imperceptible impact on core goals like long-form downloads or newsletter sign-ups. Constantly ask "why" a metric matters to avoid platform capture.

The current AI hype cycle can create misleading top-of-funnel metrics. The only companies that will survive are those demonstrating strong, above-benchmark user and revenue retention. It has become the ultimate litmus test for whether a product provides real, lasting value beyond the initial curiosity.

To evaluate AI's role in building relationships, marketers must look beyond transactional KPIs. Leading indicators of success include sustained engagement, customers volunteering more information, and recommending the experience to others. These metrics quantify brand trust and empathy—proving the brand is earning belief, not just attention.

Traditional product metrics like DAU are meaningless for autonomous AI agents that operate without user interaction. Product teams must redefine success by focusing on tangible business outcomes. Instead of tracking agent usage, measure "support tickets automatically closed" or "workflows completed."

Traditional metrics like reach are becoming obsolete. The new imperative is to measure how AI models interpret and present your brand. This involves tracking a 'share of influence' across earned media, analyst reports, and reviews, as well as monitoring AI prompt results and citations to gauge brand authority and message consistency.

While AI tools dramatically increase content production speed, true ROI is not measured in output. Leaders should track incremental engagement, conversion lift, and revenue per message. An often overlooked KPI is brand consistency—how often content passes governance checks on the first try.

In a digital-first world, measuring success by the number of assets produced is meaningless. Leaders must shift to outcome-based metrics like speed from idea to launch, brand effectiveness, and direct impact on engagement and conversion to gauge true performance.

Open and click rates are ineffective for measuring AI-driven, two-way conversations. Instead, leaders should adopt new KPIs: outcome metrics (e.g., meetings booked), conversational quality (tracking an agent's 'I don't know' rate to measure trust), and, ultimately, customer lifetime value.

Shift the mindset from a brand vs. performance dichotomy. All marketing should be measured for performance. For brand initiatives, use metrics like branded search volume per dollar spent to quantify impact and tie "fluffy" activities to tangible growth outcomes.